In this paper, we present a new algorithm for the self-localization and motion parameter extraction algorithm using 1-D perspective invariant(Cross Ratio) for mobile robots. Most of the conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Since our method uses a simple Cross Ratio, the above problems can be solved efficiently. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel side-lines are available. Also we assume that the environmental map is available for matching between the scene and the model. To extract the accurate steering angle for the mobile robot, we take advantage of geometric features such as vanishing points. Feature points for Cross Ratio are extracted robustly by a vanishing point and vertical lines, which are the intersection points between two locally parallel side-lines and vertical lines. Also we treat the local position estimation problem when feature points exist less than 4 points in the viewed scene. We demonstrate the robustness and feasibilities of our algorithms through real world experiments in indoor environments using an indoor mobile robot.
